data =
structure(list(osm_id = structure(1:5, .Label = c("17990110",
"17993246","17994983","17994985","17995338"), class ="factor"),
    name = structure(c(1L, 3L, 4L, 5L, 2L), .Label = c("109th Avenue Northeast",
   "85th Avenue Northeast","Bunker Lake Boulevard Northeast",
   "Foley Boulevard","Northdale Boulevard Northwest"), class ="factor")), row.names = c(NA,
5L), class = c("sf","data.frame"))

node_dat <- opq_osm_id(type ="node", id = '17990110')%>%
  opq_string()%>%
  osmdata_sf

node_dat_pts <- node_dat$osm_points

nrow(node_dat_pts[node_dat_pts$traffic_signals %in%"signal",])

library(osmdata)
hw <- opq("minneapolis") %>%
    add_osm_feature(key ="highway") %>%
    osmdata_sf()
signal_nodes <- opq("minneapolis") %>%
    add_osm_feature(key ="traffic_signals") %>%
    osmdata_sf()
index <- which (!is.na (hw$osm_points$traffic_signals) |
                grepl ("traffic_signals", hw$osm_points$highway)) # grepl because can be"traffic_signals:<value>"
signal_node_ids <- unique (c (signal_nodes$osm_points$osm_id, hw$osm_points$osm_id [index]))

library(dodgr)
hw <- osm_poly2line(hw)$osm_lines %>%
    weight_streetnet(keep_cols ="highway", wt_profile = 1) %>% # wt_profile irrelevant here
    dodgr_contract_graph()

net_footway <- net[net$highway =="footway", ]
table(table(c(net_footway$from_id, net_footway$to_id)))

get_related_ways <- function(wayid, bboxstring){
  related_ways_query <-
    paste0("[bbox:", bboxstring,"];\
",
          "way(id:", wayid,");\
",
          "rel(bw);\
", # get all sibling
          "way(r);\
",
          "out;")

  related_ways <- osmdata_sf(related_ways_query)
  related_ways <- related_ways$osm_lines
  related_ways <- related_ways$osm_id
  return(related_ways)
}

get_nodes_from_way <- function(wayid){
  nodes_from_way <- opq_osm_id(id = wayid,"way")%>%osmdata_sf()
  nodes_from_way <- nodes_from_way$osm_points
  nodes_from_way$geometry <- NULL
  setnames(nodes_from_way, old = 'osm_id', new = 'node_from_way_id')
  return(nodes_from_way)
}

get_intersecting_ways <- function(nodeid, bboxstring){

  node_to_intways_query <-
    paste0("[bbox:", bboxstring,"];\
",
          "node(id:", nodeid,");\
",
          "way(bn)[highway];\
",
          "out;")

  intways_from_node <- osmdata_sf(node_to_intways_query)
  intways_from_node <- intways_from_node$osm_lines
  intways_from_node$geometry <- NULL
  intways_from_node <- intways_from_node[,names(intways_from_node) %in%c("osm_id","name","highway","ref")]

  setnames(intways_from_node, old = 'osm_id', new = 'intersecting_way_id')
  setnames(intways_from_node, old = 'highway', new = 'intersecting_way_type')
  setnames(intways_from_node, old = 'ref', new = 'intersecting_way_ref')
  setnames(intways_from_node, old = 'name', new = 'intersecting_way_name')

  return(intways_from_node)
}

for(this_part in unique(cmp_osmdat$partnum)){

osm_character_ids <- as.character(cmp_osmdat$osm_id)

  # test:
  # osm_character_ids <- osm_character_ids[1:3]

  # for each parallel process to get our intersecting ways ("all ways")
  all_ways <-
    foreach(w = seq_along(osm_character_ids),
            # require list of packages from above:
            .packages = packs,
            .errorhandling ="remove", # remove data frames that throw an error
            # print the stuff:
            .verbose = TRUE) %dopar% {

              environmentIsLocked(asNamespace("curl"))
              unlockBinding(sym ="has_internet", asNamespace("curl"))
              assign(x ="has_internet", value = {function() T}, envir = asNamespace("curl"))


              this_way_id <- osm_character_ids[[w]]

              # find ways that are related to this one (same road, different segments)
              # so that we can filter these out as intersections:
              these_related_ways <- get_related_ways(this_way_id, this_bbox_string)

              # get nodes of this way:
              these_nodes_from_way <- get_nodes_from_way(this_way_id)

              # adding a column to store this way id, for easy rbind later
              # (foreach doesn't store list names?)
              these_nodes_from_way$way_id <- this_way_id

              # create an empty list to store interesecting ways for each node:
              these_intersecting_ways <- list()

              # get intersecing ways from nodes:
              for(n in seq_along(these_nodes_from_way$node_from_way_id)){
                this_node <- these_nodes_from_way$node_from_way_id[[n]]
                # put intersecting ways into our empty list (the name of the list item will be the node ID)
                these_intersecting_ways[[this_node]] <- get_intersecting_ways(this_node, this_bbox_string)
              } # end get intersecting ways from node

              # combine intersecting ways of each node into one data table:
              these_intersecting_ways_df <- rbindlist(these_intersecting_ways, idcol = 'node_from_way_id', use.names = T, fill = T)

              # get rid of intersections with this way's realtives (other segments of the same road):
              these_intersecting_ways_df <- these_intersecting_ways_df[!these_intersecting_ways_df$intersecting_way_id %in% these_related_ways,]

              # to get node information, merge intersecting ways to our node data:
              nodes_and_ways <- merge(these_intersecting_ways_df, these_nodes_from_way, by = 'node_from_way_id')

              # return node and intersection data
              return(nodes_and_ways)

            } # end foreach

  nodes_and_ways_df <- rbindlist(all_ways, use.names = T, fill = T)

  # save file, one for each part (results in 10 csvs)
  write.csv(nodes_and_ways_df,
            file = paste0("intersection_density_results/intersection-density-data-part-", this_part,".csv"), row.names = F)

} # end 10 parts

stopCluster(cl)